User Feedback Analysis of an AI-Enabled CBT Mental Health Application (Wysa) (Preprint)

BACKGROUND Digital mental health applications (apps) are rapidly becoming a common source of accessible support across the world, but their effectiveness is often influenced by limited helpfulness and engagement. There is currently a scarcity of research exploring user engagement in digital mental health applications, especially in the space of artificial intelligence (AI) guided applications. OBJECTIVE The study’s primary objective was to analyze feedback content to understand the user’s experiences of engaging with a digital mental health app. As a secondary objective, an exploratory analysis captured the types of mental health app users. METHODS This study utilized a user-led approach to understanding factors for engagement and helpfulness in digital mental health by analyzing feedback (n=7,929) reported on Google Play Store about Wysa, a mental health app (1 year period). The analysis of keywords in user feedback categorized and evaluated the reported user experience into the core domains of acceptability, usability, usefulness, and integration. The study also captured key deficits and strengths of the app, and explored salient characteristics of the types of users who benefit from accessible digital mental health support. RESULTS The analysis of user feedback found the app to be overwhelmingly positively reviewed (84.4% 5-star rating). The themes of engaging exercises, interactive interface and AI-conversational ability indicated the acceptability of the app, while the non-judgementality and ease of conversation highlighted its usability. The app’s usefulness was portrayed by themes such as improvement in mental health, convenient access and cognitive restructuring exercises. Themes of Privacy and Confidentiality underscored users’ preference for the integrated aspects of the app. Further analysis revealed 4 predominant types of individuals who shared app feedback on the store. CONCLUSIONS Users reported therapeutic elements of a comfortable, safe, and supportive environment through using the digital mental health app. Digital mental health apps may expand mental health access to those unable to access traditional forms of mental health support and treatments.

Refining the content and design of an alcohol reduction app, Drink Less, to improve its usability and effectiveness: a mixed methods approach

Background: Digital interventions have the potential to reduce alcohol consumption, although evidence on the effectiveness of apps is lacking. Drink Less is a popular, evidence-informed app with good usability, putting it in a strong position to be improved upon prior to conducting a confirmatory evaluation. This paper describes the process of refining Drink Less to improve its usability and likely effectiveness. Methods: The refinement consisted of three phases and involved qualitative and quantitative (mixed) methods: i) identifying changes to app content, based on findings from an initial evaluation of Drink Less, an updated review of digital alcohol interventions and a content analysis of user feedback; ii) designing new app modules with public input and a consultation with app developers and researchers; and iii) improving the app’s usability through user testing. Results: As a result of the updated review of digital alcohol interventions and user feedback analysis in Phase 1, three new modules: ‘Behaviour Substitution’, ‘Information about Antecedents’ and ‘Insights’, were added to the app. One existing module – ‘Identity Change’ – was removed based on the initial evaluation of Drink Less. Phases 2 and 3 resulted in changes to existing features, such as improving the navigational structure and onboarding process, and clarifying how to edit drinks and goals. Conclusions: A mixed methods approach was used to refine the content and design of Drink Less, providing insights into how to improve its usability and likely effectiveness. Drink Less is now ready for a confirmatory evaluation.

Feedback Analysis and Prevention & Control Strategy of the Real Causes of the Gallery Cracks in Super-High Arch Dam

Gallery cracks occur commonly in concrete dams, but their cracking mechanism has yet to be effectively revealed. In this paper, the actual temperature, stress change history and cracking process of a gallery area were uncovered, based on the safety monitoring data of cracks in a super-high arch dam. In addition, the basic development and change laws, as well as the corresponding cracking mechanism, were analyzed, and the real causes and influential factors of cracks at the site were revealed, which will provide a reference for the prevention of cracks in similar projects in the future.

How Teacher and Grammarly Feedback Complement One Another in Myanmar EFL Students’ Writing

Providing feedback on students’ writing is considered important by both writing teachers and students. However, contextual constraints including excess workloads and large classes pose major and recurrent challenges for teachers. To lighten the feedback burden, teachers can take advantage of a range of automated feedback tools. This paper investigated how automated feedback can be integrated into traditional teacher feedback by analyzing the focus of teacher and Grammarly feedback through a written feedback analysis of language- and content-related issues. This inquiry considered whether and how successfully students exploited feedback from different sources in their revisions and how the feedback provisions helped improve their writing performance. The study sample of texts was made up of 216 argumentative and narrative essays written by 27 low-intermediate level students at a Myanmar university over a 13-week semester. By analyzing data from the feedback analysis, we found that Grammarly provided feedback on surface-level errors, whereas teacher feedback covered both lower- and higher-level writing concerns, suggesting a potential for integration. The results from the revision analysis and pre- and post-tests suggested that students made effective use of the feedback received, and their writing performance improved according to the assessment criteria. The data were triangulated with self-assessment questionnaires regarding students’ emic perspectives on how useful they found the feedback. The pedagogical implications for integrating automated and teacher feedback are presented.

A Pharmacological Review of Siddha Classical Preparations for the Management of COVID-19 at TPEC COVID Care Centre, Vellore

Despite the threat of coronavirus infection, the Siddha system of medicine, India's traditional medicine, plays an important role in southern India, particularly in Tamilnadu. It contributed considerably not only in the first wave of Covid-19, but also in the second wave. The Government of Tamilnadu developed Siddha COVID-19 treatment centers for asymptomatic, mild, and moderate COVID-19 positive patients in 2020. The TPEC COVID Care Centre initiated at Vellore also one of the Centers that can be managed by Siddha medicines and Siddhar’s Yogam. As of July 14, 2021, about 4525 COVID positive patients had been treated with Siddha integrated treatment at Vellore alone in the first and second waves. Kaba Sura Kudineer, Thalisathy Vadagam, Amukkara Chooranam Mathirai, Bramanandha Bairavam Mathirai, and Adathodai Manapagu are indeed the five Siddha classical preparations used to manage the symptoms of COVID-19 positive patients at TPEC COVID Care Centre in Vellore. This Siddha medical practice is effective in conditions of symptoms and helps in the reduction of clinical outcomes. A pilot study at the same site confirmed the Siddha classical preparation's safety and effectiveness. A feedback analysis study performed at the same center also revealed that the above-mentioned Siddha classical preparations are beneficial in symptomatic treatment without causing any side effects. The medicines utilized in this study are typically proposed in other COVID care centers also in Tamilnadu. This review attempted to analyze the preclinical and clinical efficacy of Siddha Classical medicines used at that Centre for the management of COVID-19.

The Nonlinear Radiative Feedback Effects in the Arctic Warming

The analysis of radiative feedbacks requires the separation and quantification of the radiative contributions of different feedback variables, such as atmospheric temperature, water vapor, surface albedo, cloud, etc. It has been a challenge to include the nonlinear radiative effects of these variables in the feedback analysis. For instance, the kernel method that is widely used in the literature assumes linearity and completely neglects the nonlinear effects. Nonlinear effects may arise from the nonlinear dependency of radiation on each of the feedback variables, especially when the change in them is of large magnitude such as in the case of the Arctic climate change. Nonlinear effects may also arise from the coupling between different feedback variables, which often occurs as feedback variables including temperature, humidity and cloud tend to vary in a coherent manner. In this paper, we use brute-force radiation model calculations to quantify both univariate and multivariate nonlinear feedback effects and provide a qualitative explanation of their causes based on simple analytical models. We identify these prominent nonlinear effects in the CO2-driven Arctic climate change: 1) the univariate nonlinear effect in the surface albedo feedback, which results from a nonlinear dependency of planetary albedo on the surface albedo, which causes the linear kernel method to overestimate the univariate surface albedo feedback; 2) the coupling effect between surface albedo and cloud, which offsets the univariate surface albedo feedback; 3) the coupling effect between atmospheric temperature and cloud, which offsets the very strong univariate temperature feedback. These results illustrate the hidden biases in the linear feedback analysis methods and highlight the need for nonlinear methods in feedback quantification.

Multi-Objective Faculty Course Assignment Problem with Result and Feedback Based Uncertain Preferences

In this paper, a multi-objective faculty course allocation problem with result analysis and feedback analysis based on uncertain preferences mathematical model is presented. To deal with an uncertain model, three different ranking criteria are being used to develop: a) Expected value, b) Optimistic value, c) Dependent optimistic value criterion. These mathematical models are transformed into their corresponding deterministic forms using the basic concepts of uncertainty theory. The deterministic model of DOCM consists of fractional objectives which are converted into their linear form using Charnes and Cooper’s transformation. These deterministic formulations MOFCAP are converted into a single objective problem by using the fuzzy programming technique with linear and exponential membership functions. Further, the single objective problem for all the defined models is solved in the Lingo 18.0 software to derive the Pareto-optimal solution. The sensitivity of the models is also performed to examine the variation in the objective function due to the variation in parameters. Finally, a numerical example is given to exhibit the application and algorithm of the models.

Impact of teacher written vs. audio feedback on EFL undergraduates’ writing

Research comparing written and audio feedback from feedback analysis and students' perspectives showed the effectiveness of audio feedback in learning writing. Yet, there is a rarity of research on the impact of written and audio feedback in students' performance in writing. Therefore, the present study aimed to compare between teacher written and audio feedback in terms of its impact on students' performance in argumentative writing and students' perception of feedback. The results of this experimental study obtained from the scores of undergraduates in argumentative essays revealed that the experimental/audio feedback group of students (n.40) outperformed the control/written feedback group (n.40) in argumentative writing, which implies that audio feedback was more effective than written feedback in enhancing students' writing. Moreover, the results of the students' responses to the survey and their comments on the open questions showed that students perceived both types of feedback equally satisfactory and accessible. However, while audio feedback was more efficient in details, clarifications and personalization, written feedback was perceived clearer and easier to understand and interpret. Keywords: learning feedback; audio and written feedback; teaching writing ; L2 education.

Refining the content and design of an alcohol reduction app, Drink Less, to improve its usability and effectiveness: a mixed methods approach

Background: Digital interventions have the potential to reduce alcohol consumption, although evidence on the effectiveness of apps is lacking. Drink Less is a popular, evidence-informed app with good usability, putting it in a strong position to be improved upon prior to conducting a confirmatory evaluation. This paper describes the process of refining Drink Less to improve its usability and likely effectiveness. Methods: The refinement consisted of three phases and involved qualitative and quantitative (mixed) methods: i) identifying changes to app content, based on findings from an initial evaluation of Drink Less, an updated review of digital alcohol interventions and a content analysis of user feedback; ii) designing new app modules with public input and a consultation with app developers and researchers; and iii) improving the app’s usability through user testing. Results: As a result of the updated review of digital alcohol interventions and user feedback analysis in Phase 1, three new modules: ‘Behaviour Substitution’, ‘Information about Antecedents’ and ‘Insights’, were added to the app. One existing module – ‘Identity Change’ – was removed based on the initial evaluation of Drink Less. Phases 2 and 3 resulted in changes to existing features, such as improving the navigational structure and onboarding process, and clarifying how to edit drinks and goals. Conclusions: A mixed methods approach was used to refine the content and design of Drink Less, providing insights into how to improve its usability and likely effectiveness. Drink Less is now ready for a confirmatory evaluation.